Terrorist Speech under Bills C-51 and C-59 and the Othman Hamdan Case: The Continued Incoherence of Canada’s Approach
Bibliographic record
Abstract
It is argued that neither the approach taken to terrorist speech in Bill C-51 nor Bill C-59 is satisfactory. A case study of the Othman Hamdan case, including his calls on the Internet for “lone wolves” “swiftly to activate,” is featured, along with the use of immigration law after his acquittal for counselling murder and other crimes. Hamdan’s acquittal suggests that the new Bill C-59 terrorist speech offence and take-down powers based on counselling terrorism offences without specifying a particular terrorism offence may not reach Hamdan’s Internet postings. One coherent response would be to repeal terrorist speech offences while making greater use of court-ordered take-downs of speech on the Internet and programs to counter violent extremism. Another coherent response would be to criminalize the promotion and advocacy of terrorist activities (as opposed to terrorist offences in general in Bill C-51 or terrorism offences without identifying a specific terrorist offence in Bill C-59) and provide for defences designed to protect fundamental freedoms such as those under section 319(3) of the Criminal Code that apply to hate speech. Unfortunately, neither Bill C-51 nor Bill C-59 pursues either of these options. The result is that speech such as Hamdan’s will continue to be subject to the vagaries of take-downs by social media companies and immigration law.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".